A human visual system is capable of identifying and processing visual features with high dynamic range. For example, real-world images that have contrast ratios of 1,000,000:1 or greater can be accurately processed by the human visual cortex. However, most image acquisition devices are only capable of reproducing or capturing low dynamic range, resulting in a loss of image accuracy. The problem is ever more significant in video imaging.
There are numerous examples of creating high dynamic range images by post processing images from multiple sensors, each subject to different exposures. The resulting “blended” image is intended to capture a broader dynamic range than would be possible from a single sensor without a post-processing operation.
Typical color sensors have a color filter array (CFA) incorporated with the sensor. In a CFA sensor, each pixel has one of three color filters placed in front of it: Red, Green, or Blue. As is well-understood in the field of color sensor implementation, the raw image produced by a CFA sensor thus has a characteristic Bayer pattern. In a process that is well-understood in the field of color sensor implementation, such Bayer pattern images from color sensors are typically demosaiced to give each pixel three unique values: a Red (R) value, a Green (G) value, and a Blue (G) value. The demosaiced image is then typically color corrected (CC) to match the values of true colors in the real world (so-called “truth” or “true” color values). The CC process is well-understood in the field of color sensor implementation. The CC process entails first capturing a Bayer image of a calibrated color-square card, demosaicing this image, and then calculating a Color Correction Matrix (CCM). The CCM is stored in the camera's memory, and is then automatically multiplied by each pixel's RGB values in subsequent images, in order to correct subsequent images from the color corrected space (CCS) of the sensor to CCS of “truth”.
In a typical HDR merging process, multiple CC images are combined together to create an HDR image.
An implicit assumption in all previous HDR merging methods is that the CCM of all sensors is identical.
Despite their presence in the dynamic range, the resulting images still fail to reproduce the robust natural color of their subjects. One significant problem with traditional attempts to improve dynamic range is the inability to replicate colors which are often diluted or exaggerated as a result of traditional image processing. There is a need, therefore, for improved image processing techniques as exemplified by the following disclosure.